Regression Analysis Adjusted R2: 0.889 Std_Error: 0.061 Dep Var: ANOVA table Source Regression: 57018529 Residual: 54715626 MS: 76169755
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A multiple regression analysis produced the following tables: Predictor | Coefficients | Standard Error | t statistic | p value Intercept | -139.609 | 2548.989 | -0.054777 | 0.957154 x1 | 24.24619 | 22.25267 | 1.089586 | 0.295682 x2 | 32.10171 | 17.44559 | 1.840105 | 0.08869 Source | df | SS | MS | F | p value Regression | 2 | 302689 | 151344.5 | 1.705942 | 0.219838 Residual | 13 | 1153309 | 88716.07 | | Total | 15 | 1455998 | | | Using α = 0.01 to test the null hypothesis H0: β1 = β2 = 0, the critical F value is ___.
Adi S.
ANOVA df SS MS F Significance F Regression 3 1.9464 0.6488 2.0638 0.1102 Residual 95 29.8650 0.3144 Total 98 31.8114 Coefficients Standard Error t Stat P-value Intercept -1.5746 0.0575 -27.3770 0.0000 x1 -0.1260 0.0567 -2.2232 0.0286 x2 -0.0337 0.0304 -1.1109 0.2694 x3 -0.0095 0.0553 -0.1723 0.8636 The above table shows the regression results when estimating the multiple linear regression model relating response variable y to three predictor variables, x1, x2, and x3. At the 5% significance level, which predictor variable(s) is(are) individually significant? Only x1. Only x2. Only x3. Both x1 and x2.
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